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DOI: 10.1126/science.1121416, 506 (2006);311Science

et al.Alexander E. FarrellEthanol Can Contribute to Energy and Environmental Goals

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Ethanol Can Contribute to Energyand Environmental GoalsAlexander E. Farrell,1* Richard J. Plevin,1 Brian T. Turner,1,2 Andrew D. Jones,1 Michael OHare,2

Daniel M. Kammen1,2,3

To study the potential effects of increased biofuel use, we evaluated six representative analysesof fuel ethanol. Studies that reported negative net energy incorrectly ignored coproducts and used

some obsolete data. All studies indicated that current corn ethanol technologies are much lesspetroleum-intensive than gasoline but have greenhouse gas emissions similar to those of gasoline.However, many important environmental effects of biofuel production are poorly understood.New metrics that measure specific resource inputs are developed, but further research intoenvironmental metrics is needed. Nonetheless, it is already clear that large-scale use of ethanolfor fuel will almost certainly require cellulosic technology.

Energy security and climate change im-

peratives require large-scale substitu-

tion of petroleum-based fuels as well as

improved vehicle efficiency (1, 2). Although

biofuels offer a diverse range of promising

alternatives, ethanol constitutes 99% of all

biofuels in the United States. The 3.4 billion

gallons of ethanol blended into gasoline in2004 amounted to about 2% of all gasoline

sold by volume and 1.3% (2.5 1017 J) of its

energy content (3). Greater quantities of eth-

anol are expected to be used as a motor fuel in

the future because of two federal policies: a

/0.51 tax credit per gallon of ethanol used as

motor fuel and a new mandate for up to 7.5

billion gallons ofBrenewable fuel[ to be used

in gasoline by 2012, which was included in the

recently passed Energy Policy Act (EPACT

2005) (4, 5).

Thus, the energy and environmental impli-

cations of ethanol production are more impor-

tant than ever. Much of the analysis and publicdebate about ethanol has focused on the sign

of the net energy of ethanol: whether manu-

facturing ethanol takes more nonrenewable

energy than the resulting fuel provides (6, 7).

It has long been recognized that calculations

of net energy are highly sensitive to assump-

tions about both system boundaries and key

parameter values (8). In addition, net energy

calculations ignore vast differences between

different types of fossil energy (9). Moreover,

net energy ratios are extremely sensitive to

specification and assumptions and can produce

uninterpretable values in some important cases

(10). However, comparing across published

studies to evaluate how these assumptions af-

fect outcomes is difficult owing to the use of

different units and system boundaries across

studies. Finding intuitive and meaningful re-

placements for net energy as a performance

metric would be an advance in our ability to

evaluate and set energy policy in this impor-

tant arena.

To better understand the energy and environ-

mental implications of ethanol, we surveyed the

published and gray literature and present a

comparison of six studies illustrating the range

of assumptions and data found for the case

of corn-based (Zea mays, or maize) ethanol(1116). To permit a direct and meaningful

comparison of the data and assumptions across

the studies, we developed the Energy and

Resources Group (ERG) Biofuel Analysis Meta-

Model (EBAMM) (10). For each study, we

compared data sources and methods and param-

eterized EBAMM to replicate the published

net energy results to within half a percent. In

addition to net energy, we also calculated

metrics for greenhouse gas (GHG) emissions

and primary energy inputs (table S1 and Fig. 1).

Two of the studies stand out from the others

because they report negative net energy values

and imply relatively high GHG emissions andpetroleum inputs (11, 12). The close evaluation

required to replicate the net energy results showed

that these two studies also stand apart from the

others by incorrectly assuming that ethanol

coproducts (materials inevitably generated when

ethanol is made, such as dried distiller grains with

solubles, corn gluten feed, and corn oil) should

not be credited with any of the input energy and

by including some input data that are old and

unrepresentative of current processes, or so

poorly documented that their quality cannot be

evaluated (tables S2 and S3).

Sensitivity analyses with EBAMM and else-

where show that net energy calculations are

most sensitive to assumptions about coproduct

allocation (17). Coproducts of ethanol have

positive economic value and displace compet-

ing products that require energy to make. There-

fore, increases in corn ethanol production to

meet the requirements of EPACT 2005 will

lead to more coproducts that displace whole

corn and soybean meal in animal feed, and the

energy thereby saved will partly offset the en-

ergy required for ethanol production (5, 18).

The studies that correctly accounted for this

displacement effect reported that ethanol and

coproducts manufactured from corn yielded a

positive net energy of about 4 MJ/l to 9 MJ/l

The study that ignored coproducts but used

recent data found a slightly positive net energy

for corn ethanol (13). However, comparisons

of the reported data are somewhat misleading

because of many incommensurate assumptions

across the studies.

We used EBAMM to (i) add coproduct

credit where needed, (ii) apply a consistent

system boundary by adding missing param-eters (e.g., effluent processing energy) and

dropping extraneous ones (e.g., laborer food

energy), (iii) account for different energy types,

and (iv) calculate policy-relevant metrics (19)

Figure 1 shows both published and commensu-

rate values as well as equivalent values for the

reference, conventional gasoline.

The published results, adjusted for commen-

surate system boundaries, indicate that with

current production methods corn ethanol dis-

places petroleum use substantially; only 5 to

26% of the energy content is renewable. The

rest is primarily natural gas and coal (Fig. 2)

The impact of a switch from gasoline toethanol has an ambiguous effect on GHG

emissions, with the reported values ranging

from a 20% increase to a decrease of 32%.

These values have their bases in the same

system boundaries, but some of them rely on

data of dubious quality. Our best poin

estimate for average performance today is that

corn ethanol reduces petroleum use by about

95% on an energetic basis and reduces GHG

emissions only moderately, by about 13%

Uncertainty analysis suggests these results are

robust (10). It is important to realize that actual

performance will vary from place to place and

that these values reflect an absence of incen-tives for GHG emission control. Given ade-

quate policy incentives, the performance of

corn ethanol in terms of GHG emissions can

likely be improved (20). However, current data

suggest that only cellulosic ethanol offers large

reductions in GHG emissions.

The remaining differences among the six

studies are due to different input parameters

which are relatively easy to evaluate within the

simple, transparent EBAMM framework. For

instance, most of the difference between the

highest and lowest values for GHG emissions

in our data are due to differences in limestone

(CaCO3) application rate and energy embodied

in farm machinery (table S1). The former is truly

uncertain; data for lime application and for the

resulting GHG emissions are poor (15). In

contrast, the higher farm machinery energy values

are unverifiable and more than an order of

magnitude greater than values reported elsewhere

and calculated here, suggesting that the lower

values are more representative (10) (table S3).

This analysis illustrates the major con-

tribution of agricultural practices to life-cycle

GHG emissions (34% to 44%) and petroleum

inputs (45% to 80%) to corn ethanol, suggest-

1Energy and Resources Group, 2Goldman School of PublicPolicy, 3Renewable and Appropriate Energy Laboratory,University of California, Berkeley, CA 947203050, USA.

*To whom correspondence should be addressed. E-mail:aef@berkeley.edu

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ing that policies aimed at reducing environ-

mental externalities in the agricultural sector

may result in significantly improved environ-

mental performance of this fuel. For example,

conservation tillage reduces petroleum con-

sumption and GHG emissions as well as soil

erosion and agrichemical runoff (20, 21).

We use the best data from the six studies to

create three cases in EBAMM: Ethanol Today,

which includes typical values for the current

U.S. corn ethanol industry and requires thefewest assumptions; CO

2Intensive, which has

its basis in current plans to ship Nebraska corn

to a lignite-powered ethanol plant in North

Dakota (22); andCellulosic, which assumes that

production of cellulosic ethanol from switch-

grass becomes economic as represented in one

of the studies (16).

The Cellulosic case presented here is a pre-

liminary estimate of a rapidly evolving technol-

ogy and is designed to highlight the dramatic

reductions in GHG emissions that could be

achieved. In addition, other biofuel technologies

and production processes are in active develop-

ment and, as the data become available, should

be the subject of similar energy and environ-

mental impact assessments.

For all three cases, producing one MJ of

ethanol requires far less petroleum than is re-

quired to produce one MJ of gasoline (Fig. 2).

However, the GHG metric illustrates that the

environmental performance of ethanol varies

greatly depending on production processes. Onthe other hand, single-factor metrics may be

poor guides for policy. With the use of the

petroleum intensity metric, the Ethanol Today

case would be slightly preferred over the Cel-

lulosic case (a petroleum input ratio of 0.06

compared with 0.08); however, on the GHG

metric, the Ethanol Today case is far worse

than Cellulosic (83 compared to 11). Additional

environmental metrics are now being devel-

oped for biofuels, and a few have been applied

to ethanol production, but several key issues

remain unquantified, such as soil erosion and

the conversion of forest to agriculture (18, 20)

Looking to the future, the environmental

implications of ethanol production are likely to

grow more important, and there is a need for a

more complete set of policy-relevant metrics

In addition, future analysis of fuel ethanol

should more carefully evaluate ethanol pro-

duction from cellulosic feedstocks, not leas

because cellulosic ethanol production is un-

dergoing major technological development andbecause the cultivation of cellulosic feedstocks

is not as far advanced as corn agriculture

suggesting more potential for improvement

Such advances may enable biomass energy to

contribute a sizeable fraction of the nation_s

transportation energy, as some studies have

suggested (23, 24).

Our study yields both research and policy

recommendations. Evaluations of biofuel policy

should use realistic assumptions (e.g., the

inclusion of coproduct credits calculated by a

Fig. 1. (A) Net energy and net greenhouse gases for gasoline, six studies, andthree cases. (B) Net energy and petroleum inputs for the same. In these figures,small light blue circles are reported data that include incommensurateassumptions, whereas the large dark blue circles are adjusted values that useidentical system boundaries. Conventional gasoline is shown with red stars, andEBAMM scenarios are shown with green squares. Adjusting system boundaries

reduces the scatter in the reported results. Moreover, despite large differences innet energy, all studies show similar results in terms of more policy-relevanmetrics: GHG emissions from ethanol made from conventionally grown corn canbe slightly more or slightly less than from gasoline per unit of energy, butethanol requires much less petroleum inputs. Ethanol produced from cellulosicmaterial (switchgrass) reduces both GHGs and petroleum inputs substantially.

Fig. 2. Alternative metrics forevaluating ethanol based onthe intensity of primary en-ergy inputs (MJ) per MJ of fueland of net greenhouse gasemissions (kg CO

2-equivalent)

per MJ of fuel. For gasoline,both petroleum feedstock and

petroleum energy inputs areincluded. Other includes nu-clear and hydrological elec-tricity generation. Relative togasoline, ethanol produced to-day is much less petroleum-intensive but much more naturalgas and coal-intensive. Pro-duction of ethanol from lignite-fired biorefineries located farfrom where the corn is grownresults in ethanol with a high coal intensity and a moderate petroleum intensity. Cellulosic ethanol is expected to have an extremely low intensity forall fossil fuels and a very slightly negative coal intensity due to electricity sales that would displace coal.

REPO

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displacement method), accurate data, clearly de-

fined future scenarios, and performance metrics

relevant to policy goals like reducing greenhouse

gas emissions, petroleum inputs, and soil ero-

sion. Progress toward attaining these goals will

require new technologies and practices, such as

sustainable agriculture and cellulosic ethanol

production. Such an approach could lead to a

biofuels industry much larger than today_s that,

in conjunction with greater vehicle efficiency,

could play a key role in meeting the nation _senergy and environmental goals.

References and Notes1. T. E. Wirth, C. B. Gray, J. D. Podesta, Foreign Aff. 82, 132

(2003).2. R. A. Kerr, R. F. Service, Science 309, 101 (2005).3. S. C. Davis, S. W. Diegel, Transportation Energy Data

Book (Technical Report No. ORNL-6973, Oak RidgeNational Laboratory, Oak Ridge, TN, 2004).

4. If only cellulosic ethanol production capacity is added,

only 4.8 billion gallons will be required because ofpreferential credit provisions.

5. Implications of Increased Ethanol Production for U.S.Agriculture (Report No. 10-05, Food and Agricultural

Policy Research Institute, Univ. of Missouri, Columbia, MO,2005). Also available at www.fapri.missouri.edu/outreach/

publications/2005/FAPRI_UMC_Report_10_05.pdf.6. By convention, photosynthetic energy is ignored in this

calculation.7. H. Shapouri, J. A. Duffield, M. Wang, Trans. ASAE46, 959

(2003).

8. R. S. Chambers, R. A. Herendeen, J. J. Joyce, P. S. Penner,Science 206, 789 (1979).

9. C. J. Cleveland, Energy 30, 769 (2005).10. Materials and methods are available as supporting

material on Science Online. Additional information is

available, including the working EBAMM model, at http://socrates.berkeley.edu/rael/EBAMM.

11. T. Patzek, Crit. Rev. Plant Sci. 23, 519 (2004).12. D. Pimentel, T. Patzek, Nat. Resour. Res. 14, 65 (2005).13. M. E. D. De Oliveira, B. E. Vaughan, E. J. Rykiel,

Bioscience 55, 593 (2005).14. H. Shapouri, A. McAloon, The 2001 net energy balance

of corn ethanol (U.S. Department of Agriculture,Washington, DC, 2004). Also available at www.usda.

gov/oce/oepnu.15. M. Graboski, Fossil energy use in the manufacture of

corn ethanol (National Corn Growers Association,Washington, DC, 2002). Also available at www.ncga.

com/ethanol/main.16. M. Wang, Development and use of GREET 1.6 fuel-cycle

model for transportation fuels and vehicle technologies(Tech. Rep. ANL/ESD/TM-163, Argonne National

Laboratory, Argonne, IL, 2001). Also available atwww.transportation.anl.gov/pdfs/TA/153.pdf.

17. S. Kim, B. E. Dale, Int. J. Life Cycle Assess. 7, 237 (2002).18. M. A. Delucchi, Conceptual and methodological issues in

lifecycle analyses of transportation fuels (Tech. Rep.UCD-ITS-RR-04-45, Univ. of California, Davis, 2004).

Also available at www.its.ucdavis.edu/publications/2004/UCD-ITS-RR-04-45.pdf.

19. Factors eliminated were labor transportation, labor foodenergy, and process water energy. The first two were

deemed outside the system boundaries. Process waterenergy was included in one study but was insufficientlydocumented. Factors added were farm machinery energy,

inputs packaging, and effluent processing energy. The

metric for petroleum use included crude oil used as afeedstock for gasoline, and the metric for GHGsincluded end-use (tailpipe) fossil emissions (10).

20. S. Kim, B. E. Dale, Biomass Bioenergy 29, 426 (2005).21. E. Tegtmeier, M. Duffy, Int. J. Agric. Sustainability 2, 1

(2004).22. More information is available at www.redtrailenergyllc.com.

23. R. D. Perlack, L. L. Wright, A. Turhollow, R. Graham,B. Stokes, D. Erbach, Biomass as feedstock for abioenergy and bioproducts industry: The technical

feasibility of a billion-ton annual supply (Tech. Rep.ORNL/TM-2006/66, Oak Ridge National Laboratory,Oak Ridge, TN, 2005). Also available at http://

feedstockreview.ornl.gov/pdf/billion_ton_vision.pdf.24. L. B. Lave, W. Griffin, H. McLean, Issues Sci. Technol. 18

73 (2001).25. This research was made possible through support from

the Energy Foundation and the Karsten FamilyFoundation (both to D.M.K.) and NSFs Climate DecisionMaking Center at Carnegie Mellon University (SES-034578(to A.E.F.) and Graduate Research Fellowship program

(to A.D.J.). The authors thank J. Thompson, D. Greene,M. DeLucchi, M. Wang, and an anonymous reviewer forassistance and valuable comments.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/311/5760/506/DC1

Materials and MethodsSOM TextFigs. S1 and S2Tables S1 to S3

References

17 October 2005; accepted 9 January 200610.1126/science.1121416

Optical Detection of DNAConformational Polymorphism onSingle-Walled Carbon NanotubesDaniel A. Heller,1 Esther S. Jeng,2 Tsun-Kwan Yeung,2 Brittany M. Martinez,2

Anthonie E. Moll,2 Joseph B. Gastala,2 Michael S. Strano2*

The transition of DNA secondary structure from an analogous B to Z conformation modulates thedielectric environment of the single-walled carbon nanotube (SWNT) around which it is adsorbed.The SWNT band-gap fluorescence undergoes a red shift when an encapsulating 30-nucleotideoligomer is exposed to counter ions that screen the charged backbone. The transition isthermodynamically identical for DNA on and off the nanotube, except that the propagation lengthof the former is shorter by five-sixths. The magnitude of the energy shift is described by using aneffective medium model and the DNA geometry on the nanotube sidewall. We demonstrate thedetection of the B-Z change in whole blood, tissue, and from within living mammalian cells.

S

ingle-walled carbon nanotubes (1) are

rolled sheets of graphene with nanometer-

sized diameters that possess remarkable

photostablity (2). The semiconducting forms of

SWNTs, when dispersed by surfactants in aqueous

solution, can display distinctive near-infrared

(IR) photoluminescence (3) arising from their

electronic band gap. The band-gap energy is

sensitive to the local dielectric environment

around the SWNT, and this property can be ex-

ploited in chemical sensing, which was recently

demonstrated for the detection ofb-D-glucose (4).

Among the molecules that can bind to the

surface of SWNTs is DNA, which adsorbs as a

double-stranded (ds) complex (5). Certain DNA

oligonucleotides will transition from the native,

right-handed B form to the left-handed Z form

as cations adsorb onto and screen the nega-

tively charged backbone (69). We now show

that an analogous B-to-Z transition for a 30-

nucleotide dsDNA modulates the dielectric

environment of SWNTs and decreases their

near-IR emission energy up to 15 meV. We

have used this fluorescence signal to detect

divalent metal cations that bind to DNA and

stabilize the Z form. The thermodynamics of the

conformational change for DNA both on and off

the SWNT are nearly identical. These near-IR

ion sensors can operate in strongly scattering or

absorbing media, which we demonstrate by de-

tecting mercuric ions in whole blood, black ink,

and living mammalian cells and tissues.

Near-IR spectrofluorometry was performedon colloidally stable complexes of DNA-

encapsulated SWNTs (DNA-SWNTs) buffered

at a pH of 7.4 and synthesized by the non-

covalent binding to the nanotube sidewall (10)

of a 30base pair single-stranded DNA (ssDNA)

oligonucleotide with a repeating G-T sequence.

This ssDNA can hydrogen bond with itself to

form dsDNA. Several types of semiconducting

SWNTs are present, but as we show below

they can be identified by their characteristic

band gaps. The shift in band gap is similar for

each type of SWNT, although there is a diam-

eter dependence. After the addition of divalent

cations, we observed an energy shift in the

SWNT emission with a relative ion sensitivity

of Hg2 9 Co2 9 Ca2 9 Mg2, which is

identical for free DNA (Fig. 1A) (11). The shift

can also be observed by monitoring SWNT

photoabsorption bands (fig. S1). The fluores-

cence peak energy traces a monotonic, two-

state equilibrium profile with increasing ionic

strength for each case (12).

The removal of ions from the system via

dialysis returns the emission energy to its initial

value, which is indicative of a completely re-

versible thermodynamic transition (Fig. 1B and

1Department of Chemistry, 2Department of Chemical andBiomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

*To whom correspondence should be addressed. E-mail:strano@uiuc.edu

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ERRATUM

www.sciencemag.org SCIENCE ERRATUM POST DATE 23 JUNE 2006

CORRECTIONS&CLARIFICATIONS

Reports: Ethanol can contribute to energy and environmental goals by A. E. Farrell et al.(27 Jan. 2006, p. 506). Michael Wang of Argonne National Laboratory has raised interestingand important issues associated with greenhouse gas (GHG) emissions from corn (maize)ethanol production in this Report. The U.S. Department of Agriculture (USDA) confirmed thatthe data reported for lime application had been calculated incorrectly and kindly updatedthese values. The custom report and an updated version of the Supporting Online Materialthat discusses the issues raised in this errata in more detail are downloadable fromhttp://rael.berkeley.edu/EBAMM. The corrected data are expected to be available on the USDA

website in the coming months. In conducting a reanalysis, even larger uncertainties werediscovered in the emissions factor of lime and the emission factor for nitrous oxide (N2O)

resulting from nitrogen fertilizer application. With these refinements, the Ethanol Todaycasenow yields a point estimate of net greenhouse gases for corn ethanol at 18% below conven-tional gasoline, very close to the initially reported value of 15% below gasoline, but with anexpanded uncertainty band of 36% to +29%.

Post date 23 June 2006